Variance reduction techniques in particle-based visual contour tracking
Pattern Recognition
Vehicle Tracking Using Projective Particle Filter
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Online selection of tracking features using AdaBoost
IEEE Transactions on Circuits and Systems for Video Technology
Stabilization of parametric active contours using a tangential redistribution term
IEEE Transactions on Image Processing
Using the Particle Filter Approach to Building Partial Correspondences Between Shapes
International Journal of Computer Vision
Integrating the projective transform with particle filtering for visual tracking
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
A Probabilistic Contour Observer for Online Visual Tracking
SIAM Journal on Imaging Sciences
Particle Filtering with Region-based Matching for Tracking of Partially Occluded and Scaled Targets
SIAM Journal on Imaging Sciences
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Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters (PFs) have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter. The PF also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and background